def PlotConfusionMatrix(confusion):
labels = set()
for ((label,guess), count) in confusion.items():
labels.add(label)
labels.add(guess)
labels = [x for x in labels]
def correct(a, b):
return 1 if a == b else -1
mat = [[confusion[labels[i],labels[j]]*correct(i, j) for j in range(len(labels))] for i in range(len(labels))]
graph.hinton(np.array(mat), title="Confusion matrix", vlabels=labels, hlabels=labels)
def PlotConfusionMatrix(confusion):
labels = set()
for ((label, guess), count) in confusion.items():
labels.add(label)
labels.add(guess)
labels = [x for x in labels]
def correct(a, b):
return 1 if a == b else -1
mat = [[
confusion[labels[i], labels[j]] * correct(i, j)
for j in range(len(labels))
] for i in range(len(labels))]
graph.hinton(np.array(mat),
title="Confusion matrix",
vlabels=labels,
hlabels=labels)
world = dataset.DiscreteDistribution([(1, dist) for cat,dist in rooms.items()]) test_samples = dataset.LabelledSample(world, TEST_SAMPLES) ############################################################################# def map_classifier(sample): best = max([(dataset.SampleProbability(dist, sample), label) for label, dist in rooms.items()]) return best[1] confusion = collections.defaultdict(int) for label, sample in map(dataset.ExtractLabel, test_samples): guess = map_classifier(sample) confusion[label, guess] += 1 '''Plot confusion matrix.''' labels = set() for ((label,guess), count) in confusion.items(): labels.add(label) labels.add(guess) labels = list(sorted(labels)) def correct(a, b): return 1 if a == b else -1 mat = [[confusion[labels[i],labels[j]]*correct(i, j) for j in range(len(labels))] for i in range(len(labels))] graph.hinton(np.array(mat), title="Confusion matrix", vlabels=labels, hlabels=labels) graph.savefig(OUTPUT)